Track: Causal

When to use this track

Choose this path if you want treatment effects from a two-arm outcome model (treated/control), optionally using a propensity-score stage, and you care about effects beyond the mean (e.g., quantile effects).

Path (recommended)

1. Theory: causal objects
2. Theory: causal estimands + interpretation
3. ex09 — Causal, no X (CRP)
4. ex10 — Causal, X no PS (SB)
5. ex11 — Causal, same backend (CRP)
6. ex12 — Causal, same backend (SB)
7. ex13 — Causal, different backends (CRP)
8. ex14 — Causal, different backends (SB)

What to watch for (interpretation)

• Causal estimands are functions of the fitted outcome models; always validate fit quality before interpreting effects.
• Conditional estimands (cate(), cqte()) depend on covariate support; avoid extrapolation.

Prereqs

• Required packages and data for this page are listed in the setup chunks above.

Outputs

• This page renders model fits, diagnostics, and summary artifacts generated by package APIs.

Interpretation

• Canonical concept page: 03 Causal Inference Objects
• Treat this page as an application/example view and use the canonical page for core definitions.

Next

• Continue to the linked canonical concept page, then return for implementation-specific details.